diff --git a/NEWS.md b/NEWS.md
index 6dbf7005b150f5b00d773667cd3858c05b7ddd48..60d119cf3492af2c342d10bc03e7bef9909139a2 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -3,6 +3,8 @@
 
 - Fixes:
     + Calibration retains correlation absolute value 
+    + Calibration fixed when cal.methodi == rpc-based, apply_to == sign, 
+        eval.method == 'leave-one-out' and the correlation is not significant
 
 ### CSTools 4.0.0 
 **Submission date to CRAN: XX-12-2020**
diff --git a/R/CST_Calibration.R b/R/CST_Calibration.R
index 34607bad769369c529482a229074a70af08c3857..b58dd08ede34093076d0e493006453ea3025fbca 100644
--- a/R/CST_Calibration.R
+++ b/R/CST_Calibration.R
@@ -72,7 +72,7 @@ CST_Calibration <- function(exp, obs, cal.method = "mse_min",
 #'
 #'@author Verónica Torralba, \email{veronica.torralba@bsc.es} 
 #'@author Bert Van Schaeybroeck, \email{bertvs@meteo.be}
-#'@description Four types of member-by-member bias correction can be performed. The \code{"bias"} method corrects the bias only, the \code{"evmos"} method applies a variance inflation technique to ensure the correction of the bias and the correspondence of variance between forecast and observation (Van Schaeybroeck and Vannitsem, 2011). The ensemble calibration methods \code{"mse_min"} and \code{"crps_min"} correct the bias, the overall forecast variance and the ensemble spread as described in Doblas-Reyes et al. (2005) and Van Schaeybroeck and Vannitsem (2015), respectively. While the \code{"mse_min"} method minimizes a constrained mean-squared error using three parameters, the \code{"crps_min"} method features four parameters and minimizes the Continuous Ranked Probability Score (CRPS). The \code{"rpc-based"} method adjusts the forecast variance ensuring that the ratio of predictable components (RPC) is equal to one, as in Eade et al. (2014).
+#'@description Five types of member-by-member bias correction can be performed. The \code{"bias"} method corrects the bias only, the \code{"evmos"} method applies a variance inflation technique to ensure the correction of the bias and the correspondence of variance between forecast and observation (Van Schaeybroeck and Vannitsem, 2011). The ensemble calibration methods \code{"mse_min"} and \code{"crps_min"} correct the bias, the overall forecast variance and the ensemble spread as described in Doblas-Reyes et al. (2005) and Van Schaeybroeck and Vannitsem (2015), respectively. While the \code{"mse_min"} method minimizes a constrained mean-squared error using three parameters, the \code{"crps_min"} method features four parameters and minimizes the Continuous Ranked Probability Score (CRPS). The \code{"rpc-based"} method adjusts the forecast variance ensuring that the ratio of predictable components (RPC) is equal to one, as in Eade et al. (2014).
 #'@description Both in-sample or our out-of-sample (leave-one-out cross validation) calibration are possible.
 #'@references Doblas-Reyes F.J, Hagedorn R, Palmer T.N. The rationale behind the success of multi-model ensembles in seasonal forecasting-II calibration and combination. Tellus A. 2005;57:234-252. doi:10.1111/j.1600-0870.2005.00104.x
 #'@references Eade, R., Smith, D., Scaife, A., Wallace, E., Dunstone, N., Hermanson, L., & Robinson, N. (2014). Do seasonal-to-decadal climate predictions underestimate the predictability of the read world? Geophysical Research Letters, 41(15), 5620-5628. doi: 10.1002/2014GL061146
@@ -314,7 +314,7 @@ Calibration <- function(exp, obs, cal.method = "mse_min",
                                                    order = names(dims.fc))
         dim(var.cor.fc) <- dims.fc
       } else { ## no significant -> replacing with observed climatology
-        var.cor.fc[ , eval.dexes] <- array(data = mean(obs.tr, na.rm = na.rm), dim = dim(fc.tr))
+        var.cor.fc[ , eval.dexes] <- array(data = mean(obs.tr, na.rm = na.rm), dim = dim(fc.ev))
       }
     } else {
 	    stop("unknown calibration method: ",cal.method)